"Fossies" - the Fresh Open Source Software Archive  

Source code changes of the file "docs/_docs/quick_start.md" between
prophet-1.1.tar.gz and prophet-1.1.1.tar.gz

About: Prophet is a tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

quick_start.md  (prophet-1.1)	:	quick_start.md  (prophet-1.1.1)
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First we'll import the data:		First we'll import the data:
```python		```python
# Python		# Python
import pandas as pd		import pandas as pd
from prophet import Prophet		from prophet import Prophet
```		```
```python		```python
# Python		# Python
df = pd.read_csv('../examples/example_wp_log_peyton_manning.csv')		df = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/exampl es/example_wp_log_peyton_manning.csv')
df.head()		df.head()
```		```
<div>		<div>
<style scoped>		<style scoped>
.dataframe tbody tr th:only-of-type {		.dataframe tbody tr th:only-of-type {
vertical-align: middle;		vertical-align: middle;
}		}
.dataframe tbody tr th {		.dataframe tbody tr th {
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library(prophet)		library(prophet)
```		```
R[write to console]: Loading required package: Rcpp		R[write to console]: Loading required package: Rcpp
R[write to console]: Loading required package: rlang		R[write to console]: Loading required package: rlang
First we read in the data and create the outcome variable. As in the Python API, this is a dataframe with columns `ds` and `y`, containing the date and numeric value respectively. The ds column should be YYYY-MM-DD for a date, or YYYY-MM-DD HH:MM:SS for a timestamp. As above, we use here the log number of views to Peyt on Manning's Wikipedia page, available [here](https://github.com/facebook/prophe t/blob/main/examples/example_wp_log_peyton_manning.csv).		First we read in the data and create the outcome variable. As in the Python API, this is a dataframe with columns `ds` and `y`, containing the date and numeric value respectively. The ds column should be YYYY-MM-DD for a date, or YYYY-MM-DD HH:MM:SS for a timestamp. As above, we use here the log number of views to Peyt on Manning's Wikipedia page, available [here](https://github.com/facebook/prophe t/blob/main/examples/example_wp_log_peyton_manning.csv).
```R		```R
# R		# R
df <- read.csv('../examples/example_wp_log_peyton_manning.csv')		df <- read.csv('https://raw.githubusercontent.com/facebook/prophet/main/examples /example_wp_log_peyton_manning.csv')
```		```
We call the `prophet` function to fit the model. The first argument is the hist orical dataframe. Additional arguments control how Prophet fits the data and ar e described in later pages of this documentation.		We call the `prophet` function to fit the model. The first argument is the hist orical dataframe. Additional arguments control how Prophet fits the data and ar e described in later pages of this documentation.
```R		```R
# R		# R
m <- prophet(df)		m <- prophet(df)
```		```
Predictions are made on a dataframe with a column `ds` containing the dates for which predictions are to be made. The `make_future_dataframe` function takes the model object and a number of periods to forecast and produces a suitable datafr ame. By default it will also include the historical dates so we can evaluate in- sample fit.		Predictions are made on a dataframe with a column `ds` containing the dates for which predictions are to be made. The `make_future_dataframe` function takes the model object and a number of periods to forecast and produces a suitable datafr ame. By default it will also include the historical dates so we can evaluate in- sample fit.
```R		```R
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